Sustainability and public health nutrition at school: assessing the integration of healthy and environmentally sustainable food initiatives in Vancouver schools
Bibliographic record
Abstract
OBJECTIVE: To describe the development and application of the School Food Environment Assessment Tools and a novel scoring system to assess the integration of healthy and environmentally sustainable food initiatives in elementary and secondary schools. DESIGN: The cross-sectional study included direct observations of physical food environments and interviews with key school personnel regarding food-related programmes and policies. A five-point scoring system was then developed to assess actions across six domains: (i) food gardens; (ii) composting systems; (iii) food preparation activities; (iv) food-related teaching and learning activities; and availability of (v) healthy food; and (vi) environmentally sustainable food. SETTING: Vancouver, Canada. SUBJECTS: A purposive sample of public schools (n 33) from all six sectors of the Vancouver Board of Education. RESULTS: Schools scored highest in the areas of food garden and compost system development and use. Regular integration of food-related teaching and learning activities and hands-on food preparation experiences were also commonly reported. Most schools demonstrated rudimentary efforts to make healthy and environmentally sustainable food choices available, but in general scored lowest on these two domains. Moreover, no schools reported widespread initiatives fully supporting availability or integration of healthy or environmentally sustainable foods across campus. CONCLUSIONS: More work is needed in all areas to fully integrate programmes and policies that support healthy, environmentally sustainable food systems in Vancouver schools. The assessment tools and proposed indicators offer a practical approach for researchers, policy makers and school stakeholders to assess school food system environments, identify priority areas for intervention and track relevant changes over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".